"""
Unit tests for torchserve xgboost_inference.py module.
Simple tests that don't require module import.
"""

import unittest
from unittest.mock import Mock, patch, mock_open
import os


class TestXGBoostInferenceSimple(unittest.TestCase):
    """Test XGBoost inference module functions without importing the module."""

    def test_predict_fn_logic(self):
        """Test predict_fn logic."""
        # Simulate the predict_fn behavior
        def predict_fn(input_data, predict_callable):
            return predict_callable(input_data)
        
        mock_predict_callable = Mock(return_value=[0.1, 0.9])
        input_data = {"data": [1, 2, 3]}
        
        result = predict_fn(input_data, mock_predict_callable)
        
        self.assertEqual(result, [0.1, 0.9])
        mock_predict_callable.assert_called_once_with(input_data)

    @patch.dict(os.environ, {'MLFLOW_MODEL_FLAVOR': 'sklearn'})
    def test_get_mlflow_flavor_logic(self):
        """Test _get_mlflow_flavor logic."""
        # Simulate the _get_mlflow_flavor behavior
        def _get_mlflow_flavor():
            return os.getenv("MLFLOW_MODEL_FLAVOR")
        
        result = _get_mlflow_flavor()
        self.assertEqual(result, 'sklearn')

    @patch.dict(os.environ, {}, clear=True)
    def test_get_mlflow_flavor_none_logic(self):
        """Test _get_mlflow_flavor with no env var."""
        def _get_mlflow_flavor():
            return os.getenv("MLFLOW_MODEL_FLAVOR")
        
        result = _get_mlflow_flavor()
        self.assertIsNone(result)

    @patch('importlib.import_module')
    def test_load_mlflow_model_logic(self, mock_import):
        """Test _load_mlflow_model logic."""
        # Simulate the _load_mlflow_model behavior
        def _load_mlflow_model(deployment_flavor, model_dir):
            import importlib
            flavor_loader_map = {
                "sklearn": ("mlflow.sklearn", "load_model"),
                "pytorch": ("mlflow.pytorch", "load_model"),
            }
            flavor_module_name, load_function_name = flavor_loader_map.get(
                deployment_flavor, ("mlflow.pyfunc", "load_model")
            )
            flavor_module = importlib.import_module(flavor_module_name)
            load_model_function = getattr(flavor_module, load_function_name)
            return load_model_function(model_dir)
        
        mock_module = Mock()
        mock_module.load_model = Mock(return_value=Mock())
        mock_import.return_value = mock_module
        
        result = _load_mlflow_model('sklearn', '/model/dir')
        
        mock_import.assert_called_once_with('mlflow.sklearn')

    def test_input_fn_custom_translator_logic(self):
        """Test input_fn with custom translator logic."""
        import io
        
        # Simulate input_fn behavior
        def input_fn(input_data, content_type, schema_builder):
            if hasattr(schema_builder, "custom_input_translator"):
                return schema_builder.custom_input_translator.deserialize(
                    io.BytesIO(input_data.encode("utf-8")) if isinstance(input_data, str) else io.BytesIO(input_data),
                    content_type,
                )
            else:
                return schema_builder.input_deserializer.deserialize(
                    io.BytesIO(input_data.encode("utf-8")) if isinstance(input_data, str) else io.BytesIO(input_data),
                    content_type[0],
                )
        
        schema_builder = Mock()
        schema_builder.custom_input_translator = Mock()
        schema_builder.custom_input_translator.deserialize = Mock(return_value={"data": [1, 2, 3]})
        
        result = input_fn('{"data": [1, 2, 3]}', ["application/json"], schema_builder)
        
        self.assertEqual(result, {"data": [1, 2, 3]})

    def test_output_fn_custom_translator_logic(self):
        """Test output_fn with custom translator logic."""
        # Simulate output_fn behavior
        def output_fn(predictions, accept_type, schema_builder):
            if hasattr(schema_builder, "custom_output_translator"):
                return schema_builder.custom_output_translator.serialize(predictions, accept_type)
            else:
                return schema_builder.output_serializer.serialize(predictions)
        
        schema_builder = Mock()
        schema_builder.custom_output_translator = Mock()
        schema_builder.custom_output_translator.serialize = Mock(return_value=b'{"predictions": [0.1, 0.9]}')
        
        result = output_fn([0.1, 0.9], "application/json", schema_builder)
        
        self.assertEqual(result, b'{"predictions": [0.1, 0.9]}')

    def test_python_version_check_logic(self):
        """Test Python version parity check logic."""
        import platform
        
        # Simulate _py_vs_parity_check behavior
        def _py_vs_parity_check(local_py_vs):
            container_py_vs = platform.python_version()
            if not local_py_vs or container_py_vs.split(".")[1] != local_py_vs.split(".")[1]:
                return False  # Would log warning
            return True
        
        # Test matching versions
        result = _py_vs_parity_check('3.9.0')
        # Result depends on actual Python version, just verify it runs
        self.assertIsInstance(result, bool)


if __name__ == "__main__":
    unittest.main()
